Reliability Assessment of Neural Networks in GPUs: A Framework For Permanent Faults Injections

TL;DR

This paper introduces a novel framework using fault injection to evaluate the reliability of CNNs on GPUs, specifically focusing on the impact of permanent hardware faults on neural network performance.

Contribution

It presents the first comprehensive environment for assessing CNN reliability on GPUs considering permanent faults through targeted fault injection campaigns.

Findings

First to evaluate permanent faults in GPU-based CNNs

Identifies critical GPU components affecting reliability

Provides insights for designing fault-tolerant neural systems

Abstract

Currently, Deep learning and especially Convolutional Neural Networks (CNNs) have become a fundamental computational approach applied in a wide range of domains, including some safety-critical applications (e.g., automotive, robotics, and healthcare equipment). Therefore, the reliability evaluation of those computational systems is mandatory. The reliability evaluation of CNNs is performed by fault injection campaigns at different levels of abstraction, from the application level down to the hardware level. Many works have focused on evaluating the reliability of neural networks in the presence of transient faults. However, the effects of permanent faults have been investigated at the application level, only, e.g., targeting the parameters of the network. This paper intends to propose a framework, resorting to a binary instrumentation tool to perform fault injection campaigns, targeting different components inside the GPU, such as the register files and the functional units. This environment allows for the first time assessing the reliability of CNNs deployed on a GPU considering the presence of permanent faults.